Multiple instance learning methods such as MI-SVM [1]} have been extensively used
for large-scale weakly supervised object detection. In a standard multiple instance learning framework,
latent bounding box labels and the appearance model are estimated jointly in an
alternating optimization process with the constraint that at least one bounding
box should be positive in each image. Alternating optimization combined with
modern deep neural network architecture is a dominant technique in the
literature showing the state-of-the-art performance in
WSOD [2]}, [3]}, [4]}, [5]}.
Ilse  [6]} propose an attention-based
deep multiple instance learning architecture where bag label probability distribution is learned by
neural networks. More related to our work are a class of WSOD algorithms that
use knowledge transfer from a fully annotated base dataset to aid WSOD for a set
of novel classes [2]}, [3]}, [9]}, [10]}.
In [3]}, a class agnostic objectness score is learned
from the base dataset and is utilized to guide the multiple instance learning optimization steering
toward objects and away from the background. These methods rely on a relatively
large dataset to learn novel categories.
